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## ANALYSIS.md

      
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                N3mes1s
                / ANALYSIS.md
            
            
              Last active
              April 12, 2026 19:20
            
              
                CPU-Z 2.19 Supply Chain Attack Analysis (April 2026) - Trojanized DLL Sideloading with Zig-compiled CRYPTBASE.dll, IPv6-encoded .NET deserialization, MSBuild persistence
              
          
    CPU-Z 2.19 Supply Chain Attack - Malware Analysis Report

Date: 2026-04-10
Analyst: nemesis
Classification: Trojan / Backdoor (Alien RAT variant)
Severity: CRITICAL
Campaign ID: CityOfSin (extracted from C2 callback UTM parameters)
Scope: CPUID official domain compromise affecting CPU-Z, HWMonitor, HWMonitor Pro, PerfMonitor 2, powerMAX + separately FileZilla
Status: Breach confirmed and fixed by CPUID; site was compromised ~6 hours on April 9-10, 2026
CPUID Statement: "A secondary feature (a side API) was compromised for approximately six hours [...] causing the main website to randomly display malicious links. Our signed original files were not compromised."

  
## llm-wiki.md

      
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                karpathy
                / llm-wiki.md
            
            
              Created
              April 4, 2026 16:25
            
              
                llm-wiki
              
          
    LLM Wiki

A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

  
## alwaysshowtooltipplugin.ts
import type {
    Chart,
    ChartType,
    Plugin,
    ChartConfiguration,
    VisualElement,
} from 'chart.js'

export interface AlwaysShowTooltipPluginOptions {
    color?: string

## llm-wiki.md

      
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                rohitg00
                / llm-wiki.md
            
            
              Last active
              April 12, 2026 19:19
                — forked from karpathy/llm-wiki.md
            
              
                LLM Wiki v2 — extending Karpathy's LLM Wiki pattern with lessons from building agentmemory
              
          
    LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory, a persistent memory engine for AI coding agents.
This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.
What the original gets right

The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.

  
## add_to_zshrc.sh
# -----------------------------------------------------------------------------
# AI-powered Git Commit Function
# Copy paste this gist into your ~/.bashrc or ~/.zshrc to gain the `gcm` command. It:
# 1) gets the current staged changed diff
# 2) sends them to an LLM to write the git commit message
# 3) allows you to easily accept, edit, regenerate, cancel
# But - just read and edit the code however you like
# the `llm` CLI util is awesome, can get it here: https://llm.datasette.io/en/stable/

gcm() {

## llms_on_cpu.md

      
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                iam-veeramalla
                / llms_on_cpu.md
            
            
              Created
              March 8, 2026 19:08
            
              
                Run LLMs locally on CPU Architecture
              
          
    Run LLMs Locally Using llama.cpp

This tutorial shows how to run Large Language Models locally on your laptop using llama.cpp and GGUF models.
It works on:

macOS
Linux
Windows
	import type {
	Chart,
	ChartType,
	Plugin,
	ChartConfiguration,
	VisualElement,
	} from 'chart.js'

	export interface AlwaysShowTooltipPluginOptions {
	color?: string
	# -----------------------------------------------------------------------------
	# AI-powered Git Commit Function
	# Copy paste this gist into your ~/.bashrc or ~/.zshrc to gain the `gcm` command. It:
	# 1) gets the current staged changed diff
	# 2) sends them to an LLM to write the git commit message
	# 3) allows you to easily accept, edit, regenerate, cancel
	# But - just read and edit the code however you like
	# the `llm` CLI util is awesome, can get it here: https://llm.datasette.io/en/stable/

	gcm() {